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從需求輸入編譯確定性的 Python 鎖定檔案

將 Python 依賴輸入解析為確定性的鎖定檔案,並同步環境,無需手動編輯傳遞性固定版本。

從需求輸入編譯確定性的 Python 鎖定檔案 是什麼?

此技能專注於使用 pip-tools 工具,將 Python 專案的依賴輸入文件(例如 requirements.in)編譯為確定性的鎖定檔案(例如 requirements.txt)。這能確保在不同環境或時間點安裝時,所有直接與傳遞性依賴的版本完全相同,從而避免「在我機器上可以運行」的問題。技能支援的平臺包括任何安裝了 Python 和 pip-tools 的環境,建議在虛擬環境中執行。透過自動解析依賴樹並固定版本,開發者無需手動編輯鎖定檔案,只需維護易於閱讀的輸入文件。此技能與 Claude Code、Cursor 或 Codex 等 AI 代理整合時,可自動化依賴管理流程,提升專案的可重複性與穩定性。

相容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/agentskillexchange/skills/tree/main/skills/compile-deterministic-python-lock-files-from-requirements-inputs-with-pip-tools

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說明文件

從需求輸入編譯確定性的 Python 鎖定檔案(使用 pip-tools)

將 Python 依賴輸入解析為確定性的鎖定檔案,並同步環境,無需手動編輯傳遞性固定版本。

先決條件

Python、pip-tools、依賴輸入文件

安裝

使用符合您環境的上游安裝或設定路徑:

  • (venv) $ python -m pip install pip-tools
  • 如果已安裝 pipx,則使用 pipx run --spec pip-tools pip-compile
  • pip-compile 應該在與您的專案相同的虛擬環境中執行
  • 官方標準做法是使用 pip-compile 來固定依賴

上游的要求和注意事項:

  • 即使您已將它們固定,也要保持固定。您有固定它們,對吧?(在建立 Python 專案時)
  • 與 pip 類似,pip-tools 必須安裝在您專案的每個
  • 虛擬環境 中:

基本用法或入門說明:

文件

來源

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

Academic Paper Citation Network Mapper

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ACF Extended for Advanced Custom Fields Workflows

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ACF to Gutenberg Block Migrator

Converts Advanced Custom Fields field groups into native Gutenberg blocks using the ACF Block API v2 and @wordpress/scripts build pipeline. Maps ACF repeaters, groups, and flexible content to InnerBlocks and block attributes with server-side rendering via acf_register_block_type().

Activepieces Open Source Workflow Automation

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ActivityWatch Cross-Platform Automated Time Tracker and Producti

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act Local GitHub Actions Runner

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Add Claude design-system and accessibility workflows with UX/UI

Use UX/UI Agent Skills when Claude should generate tokens, component specs, accessibility audits, and framework-specific UI code from a repeatable design workflow.

Add executable smoke tests for shell scripts and CLIs before ref

Use Bats-core when an agent needs to turn fragile shell scripts or command-line workflows into something it can verify repeatedly after edits. The agent writes focused Bash tests for success paths, failure paths, and output contracts, then runs them locally or in CI before a refactor, release, or incident fix goes out.

Add graph-backed memory and context retrieval to agent workflows

Use Cognee to ingest project knowledge into graph and vector memory so agents can retrieve durable context across sessions and workflows.

Add IDE-style semantic code navigation and symbol-safe refactors

Give a coding agent symbol-aware lookup, cross-file rename, and structural edit tools before it starts making brittle text-only changes.

Add managed long-term memory to LangGraph agents with LangMem

Give LangGraph agents memory management and search tools so they can store, retrieve, and update durable facts across sessions.

Add multimodal lifelong memory to MCP and Python agents with Sim

Use SimpleMem to store, compress, index, and retrieve text or multimodal memories for agents through MCP or Python integrations.

Add persistent project memory and hook-driven synthesis with Cla

Keep Claude Code sessions grounded in prior decisions, project context, and daily handoff notes instead of starting from zero every time.

Add Postgres-native vector retrieval to agent and RAG workflows

Store embeddings beside application data in Postgres, create vector indexes, and query nearest neighbors for semantic search, RAG, recommendations, or agent memory retrieval.

Address GitHub PR review comments from the current branch with g

Find the open PR for the current branch, gather unresolved review comments, and drive a focused comment-resolution workflow with gh-authenticated context.

Add runtime guardrails to TypeScript agents with VoltAgent

Use VoltAgent to intercept, validate, and enforce input/output policies in TypeScript agent workflows.

Add temporal context memory to agent workflows with Zep

Use Zep as an external context layer for agents that need to store events, assemble temporal graph context, and retrieve relevant memory before model calls.

Adobe Firefly API Batch Image Generator

Calls Adobe Firefly

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